As shown in FIG. 1, a wireless communication system 10 comprises elements such as client terminal or mobile station 12 and base stations 14. Other network devices which may be employed, such as a mobile switching center, are not shown. In some wireless communication systems there may be only one base station and many client terminals while in some other communication systems such as cellular wireless communication systems there are multiple base stations and a large number of client terminals communicating with each base station.
As illustrated, the communication path from the base station (BS) to the client terminal direction is referred to herein as the downlink (DL) and the communication path from the client terminal to the base station direction is referred to herein as the uplink (UL). In some wireless communication systems the client terminal or mobile station (MS) communicates with the BS in both DL and UL directions. For instance, this is the case in cellular telephone systems. In other wireless communication systems the client terminal communicates with the base stations in only one direction, usually the DL. This may occur in applications such as paging.
The base station to which the client terminal is communicating with is referred as the serving base station. In some wireless communication systems the serving base station is normally referred as the serving cell. The terms base station and a cell may be used interchangeably herein. In general, the cells that are in the vicinity of the serving cell are called neighbor cells. Similarly, in some wireless communication systems a neighbor base station is normally referred as a neighbor cell. A neighbor cell that is a candidate for handover or reselection is referred as a target cell herein. Whenever a cell becomes a serving cell for a client terminal, that cell is referred as visited cell herein.
In order to improve system capacity, peak data rate and coverage reliability, the signal transmitted to a particular user can be adapted by the base station to compensate for the signal quality variations. This process is known as link adaptation. In cellular wireless communication systems, the quality of the signal received by a client terminal depends on the channel quality from the serving cell, the level of interference from neighbor cells, and the noise level. One of the well known techniques for link adaptation is the Adaptive Modulation and Coding (AMC). With AMC, the modulation and coding formats are changed to match the prevailing radio channel capacity for each client terminal. In a system with AMC, client terminals close to the base station may be typically assigned higher-order modulation and high code rate, for example 64-Quadrature Amplitude Modulation (QAM) with high code rate, but the modulation-order and the code rate may decrease as the distance from the base station increases.
In the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) wireless communication system, the base station typically selects the modulation scheme and code rate depending on a prediction of the downlink channel conditions. An important input to this selection process is the Channel Quality Indicator (CQI) feedback transmitted by the client terminal in the uplink. The CQI feedback is an indication of the data rate which can be supported by the channel, taking into account the Signal-to-Interference-plus-Noise Ratio (SINR) and the capabilities of the client terminal's receiver.
The CQI feedback is derived from the downlink received signal quality, which may be based on the measurements of the downlink reference signals which are transmitted by the base station. The client terminal indicates the highest Modulation and Coding Scheme (MCS) that it can decode with a Block Error Rate (BLER) probability not exceeding a specified level, e.g., 10%. Thus the information received by the base station takes into account the capabilities of the client terminal's receiver, and not just the prevailing radio channel quality. The highest selected MCS is mapped to one of the possible CQI values based on mapping specified by the wireless communication system specification. Hence a client terminal that is designed with advanced signal processing algorithms can report a higher CQI and may achieve a higher data rate.
A key issue in system level performance is the need to predict a BLER from the instantaneous SINR for each subcarrier of an OFDM symbol. For example, the same SINR under static and various fading conditions may yield different BLER depending on the specific conditions. Therefore, a link quality model such as Effective Exponential SINR Mapping (EESM) may be used for mapping a set of instantaneous SINRs to a single effective SINR for predicting the BLER under any given channel conditions. The objective of EESM is to find a compression function that maps the set of SINRs to a single value that is a good predictor of the actual BLER for a given MCS. FIG. 2 illustrates the EESM methodology. Note that EESM is one of the commonly used methods for the link quality model and other models are possible. For illustrating the present invention the EESM link model is used.
The mapping of the effective SINR value to the corresponding BLER value may use either a look-up table for the mapping function or use an approximate analytical expression. The EESM method estimates the effective SINR using the following formula:
                              γ          eff                =                              EESM            ⁡                          (                                                γ                  k                                ,                β                            )                                =                                    -              β                        ⁢                                                  ⁢                          ln              ⁡                              (                                                      1                    N                                    ⁢                                                            ∑                                              k                        =                        0                                                                    N                        -                        1                                                              ⁢                                          ⅇ                                              -                                                                              γ                            k                                                    β                                                                                                                    )                                                                        EQ        .                                  ⁢                  (          1          )                    Where, γ is a vector [γ0, γ1, . . . , γN-1] of the per-subcarrier SINR values, which may be typically different in a frequency selective channel. The parameter β may be determined for each CQI and this value is used to adjust EESM function to compensate the difference between the actual BLER and the predicted BLER.
The β values are specific to each CQI and are optimized based on offline simulations performed across different fading profiles and SINRs, and are used to establish the mapping between instantaneous SINR and effective SINR. For example, in 3GPP LTE wireless communication system, there are 16 possible CQIs to be considered for channel quality feedback reports as shown in the table contained in FIG. 3. Using the Additive White Gaussian Noise (AWGN) performance charts as a reference, the BLER value of each CQI for different fading propagation conditions is mapped to an AWGN equivalent SINR. The AWGN SINR for each CQI is denoted as SINRAWGN.
During normal operation, the γeff needs to be computed from the set of per subcarrier SINRs for each possible value of CQI and the corresponding β value. In the case of a 3GPP LTE wireless communication system, there may be up to 1200 subcarriers when using a 20 MHz channel bandwidth. Therefore, EQ. (1) may need to be evaluated for up to 1200 subcarriers (N=1200) and the computations have to be repeated for 16 different CQIs and the corresponding β. This is a non-trivial task that can be resource intensive.
The final output of the EESM process is the highest CQI with an effective SINR greater than SINRAWGN as an optimum CQI for given channel conditions. Conventionally, two methods are used to reduce complexity of γeff computations. One method is to estimate the effective SINR starting from the highest CQI and decrement the CQI index such that at most within 15 iterations the optimum CQI can be selected in case of 3GPP LTE wireless communication system. The computed effective SINR γeff is compared against the SINRAWGN for the current CQI candidate. If the computed γeff is lower than the SINRAWGN for the current CQI, then the γeff computation is performed for the next lower CQI and its corresponding β. This process is repeated until the point where the γeff is equal to or greater than the required SINRAWGN for the current CQI. Another method for finding the optimum CQI with reduced complexity is to perform a binary search or other search mechanisms such that, in case of 15 different CQIs, within five steps the best CQI may be selected as shown with example in FIG. 4. Such techniques may be computer processor intensive and time consuming. This can be detrimental to the operation of the wireless device and the overall communication system.